Intensity modulated radiation therapy has been shown to improve radiotherapy dose distributions when applied to lung treatments, especially if the delivery can be registered to the patient's breathing cycle. However, many technical issues associated with breathing-registered IMRT treatment planning have not yet been addressed, and comparisons between potential planning and delivery approaches are lacking. The primary goal of this project is to determine the tradeoffs between increased planning and delivery complexity vs. improvements in cumulative patient dose distributions and the expected impact on treatment outcomes. In an interdisciplinary collaboration, a shared cross-platform toolset, techniques, and test data sets will be developed and eventually publicly archived for use by other researchers. Within this framework, planning techniques to simulate a range of potential clinical options, from simple to complex, will be developed, including: simple margins, accounting for tumor motion in a statistical way, gating, and fully synchronizing IMRT delivery with breathing-phase. It is hypothesized that a significant fraction of simulated lung patients (who cannot achieve breath-hold techniques) will have a predicted outcome benefit from the use of breathing-registered planning and delivery. In Specific Aim SA #1 (4-D optimization), temporo-spatial ("4-D") IMRT planning and optimization methods for all relevant delivery techniques will be developed. In SA #2 (Management of uncertainties and daily variations), those methods will be generalized to explicitly account for uncertainties and day-to-day variations. In SA #3 (Clinical impact), the potential impact of the techniques in terms of likely dosimetric and outcome improvements will be estimated for a statistically significant cohort of simulated patient plans. This research will provide, firstly, guidance for understanding the potential outcomes impact of 4-D radiotherapy treatments; secondly, robust solutions to challenging 4-D treatment planning problems; and thirdly, shared data and software tools for other researchers in the area of 4-D planning. [unreadable] [unreadable] [unreadable]